Approximate query processing over static sets and sliding windows

Indexing of static and dynamic sets is fundamental to a large set applications such as information retrieval caching. Denoting the characteristic vector by B , we consider problem encoding multisets support approximate versions operations rank ( i ) (i.e., computing ∑ j ≤ [ ] select finding min ⁡ { p | ≥ } queries. We study multiple types approximations (allowing an error in query or result) present lower bounds succinct data structures for several variants problem. also extend our model sliding windows, which process stream elements compute suffix sums . This generalization window summation that allows user specify size at time Here, provide algorithm supports updates queries constant while requiring just 1 + o factor more space than fixed-window algorithms.